class_docstr: Gated Graph Convolution layer from `Gated Graph Sequence
  Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`__
  
  .. math::
      h_{i}^{0} &= [ x_i \| \mathbf{0} ]
  
      a_{i}^{t} &= \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t}
  
      h_{i}^{t+1} &= \mathrm{GRU}(a_{i}^{t}, h_{i}^{t})
  
  Parameters
  ----------
  in_feats : int
      Input feature size; i.e, the number of dimensions of :math:`x_i`.
  out_feats : int
      Output feature size; i.e., the number of dimensions of :math:`h_i^{(t+1)}`.
  n_steps : int
      Number of recurrent steps; i.e, the :math:`t` in the above formula.
  n_etypes : int
      Number of edge types.
  bias : bool
      If True, adds a learnable bias to the output. Default: ``True``.
  
  Example
  -------
  >>> import dgl
  >>> import numpy as np
  >>> import torch as th
  >>> from dgl.nn import GatedGraphConv
  >>>
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
  >>> feat = th.ones(6, 10)
  >>> conv = GatedGraphConv(10, 10, 2, 3)
  >>> etype = th.tensor([0,1,2,0,1,2])
  >>> res = conv(g, feat, etype)
  >>> res
  tensor([[ 0.4652,  0.4458,  0.5169,  0.4126,  0.4847,  0.2303,  0.2757,  0.7721,
          0.0523,  0.0857],
          [ 0.0832,  0.1388, -0.5643,  0.7053, -0.2524, -0.3847,  0.7587,  0.8245,
          0.9315,  0.4063],
          [ 0.6340,  0.4096,  0.7692,  0.2125,  0.2106,  0.4542, -0.0580,  0.3364,
          -0.1376,  0.4948],
          [ 0.5551,  0.7946,  0.6220,  0.8058,  0.5711,  0.3063, -0.5454,  0.2272,
          -0.6931, -0.1607],
          [ 0.2644,  0.2469, -0.6143,  0.6008, -0.1516, -0.3781,  0.5878,  0.7993,
          0.9241,  0.1835],
          [ 0.6393,  0.3447,  0.3893,  0.4279,  0.3342,  0.3809,  0.0406,  0.5030,
          0.1342,  0.0425]], grad_fn=<AddBackward0>)
code: def __init__(self, in_feats, out_feats, n_steps, n_etypes, bias=True):
      super(GatedGraphConv, self).__init__()
      assert (
          in_feats <= out_feats
      ), "out_feats must be not less than in_feats"
      self._in_feats = in_feats
      self._out_feats = out_feats
      self._n_steps = n_steps
      self._n_etypes = n_etypes
      self.linears = nn.ModuleList(
          [nn.Linear(out_feats, out_feats) for _ in range(n_etypes)]
      )
      self.gru = nn.GRUCell(out_feats, out_feats, bias=bias)
      self.reset_parameters()
  
file_path: dgl/python/dgl/nn/pytorch/conv/gatedgraphconv.py
imports: import torch
  from torch import nn
  from torch.nn import init
  from ....None import function
label_desc: Use image node_id 1 to create a new GatedGraphConv object from inherited base classes: nn with example: obj = GatedGraphConv(in_feats, out_feats, n_steps, n_etypes, bias)